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Download Designing and Implementing a Data Science Solution on Azure Exam Dumps

NEW QUESTION 39
You are using Azure Machine Learning to train machine learning models. You need a compute target on which to remotely run the training script. You run the following Python code:
DP-100-eedf5cd31b3638edf295eda06f094541.jpg
DP-100-7da4f3da3da4b008a30d1c2c30bf769b.jpg

Answer:

Explanation:
DP-100-7e6a169670e5c7c2883c330605dffe78.jpg

 

NEW QUESTION 40
You need to define a process for penalty event detection.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
DP-100-89afccf1eb33346605402144266703cb.jpg

Answer:

Explanation:
DP-100-29f767af5a19ff0911e778398c87d313.jpg

 

NEW QUESTION 41
You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.
The following image displays the results dataset output:
DP-100-8a9837ce70e967d7f0f8e0f8cbb1b9dd.jpg
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.
NOTE: Each correct selection is worth one point.
DP-100-0d2aa7c6174ea7e45883d50c2b48bb30.jpg

Answer:

Explanation:
DP-100-f11e60a7e666f2b65166fd0207ef0811.jpg
Explanation
DP-100-21a425a2cd0201aeb222d5675354b7c1.jpg
Box 1: Boosted Decision Tree Regression
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Box 2:
Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

 

NEW QUESTION 42
You are performing clustering by using the K-means algorithm.
You need to define the possible termination conditions.
Which three conditions can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. The residual sum of squares (RSS) falls below a threshold.
  • B. The sum of distances between centroids reaches a maximum.
  • C. Centroids do not change between iterations.
  • D. The residual sum of squares (RSS) rises above a threshold.
  • E. A fixed number of iterations is executed.

Answer: B,D,E

 

NEW QUESTION 43
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.
DP-100-3ec0c96d693024c88a32ca2301972b1a.jpg
DP-100-a8c5e068c472ce3597ca76e125277de0.jpg

Answer:

Explanation:
DP-100-f00668d08568283d59acc932be2dad6a.jpg

 

NEW QUESTION 44
......

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